Abstract
The monitoring of farm animals and the automatic recognition of deviant behavior have recently become increasingly important in farm animal science research and in practical agriculture. The aim of this study was to develop an approach to automatically predict behavior and posture of sows by using a 2D image-based deep neural network (DNN) for the detection and localization of relevant sow and pen features, followed by a hierarchical conditional statement based on human expert knowledge for behavior/posture classification. The automatic detection of sow body parts and pen equipment was trained using an object detection algorithm (YOLO V3). The algorithm achieved an Average Precision (AP) of 0.97 (straw rack), 0.97 (head), 0.95 (feeding trough), 0.86 (jute bag), 0.78 (tail), 0.75 (legs) and 0.66 (teats). The conditional statement, which classifies and automatically generates a posture or behavior of the sow under consideration of context, temporal and geometric values of the detected features, classified 59.6% of the postures (lying lateral, lying ventral, standing, sitting) and behaviors (interaction with pen equipment) correctly. In conclusion, the results indicate the potential of DNN toward automatic behavior classification from 2D videos as potential basis for an automatic farrowing monitoring system.
Highlights
The monitoring of farm animals and the automatic detection of abnormal behavior has recently gained considerable importance in farm animal science research
The data of six cameras was stored on one Synology R network attached storage (NAS) with 8 TB storage space via Ethernet cable (25 m) connection with Power over Ethernet (PoE)
Right or left; legs stretched out, teats visible Lying on the belly, front legs under the sow, teats mostly not visible Extended front legs, front claws touching the ground Standing or moving using all four legs head is next to the coordinates of the last detected trough
Summary
The monitoring of farm animals and the automatic detection of abnormal behavior has recently gained considerable importance in farm animal science research. The relevant data-output can be backed up to a small amount of storage, by saving as e.g., table Such capabilities are consistent with most of the characteristics required for a sensor to assess animal welfare (Rushen et al, 2012) and have already ensured that several research approaches have been investigated using different camera systems and algorithms of varying complexity (examples are described in detail in the following passages). These approaches can be separated by different camera types (e.g., 2D images, 3D depth images), and different type of monitoring (e.g., single pigs with detailed behavioral observation (mainly sows) or multiple pigs and animal interactions)
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